A Dual-layer User Model based Cognitive System for User-Adaptive Service Robots
Published in ROMAN 2011, 2011
A Dual-layer User Model based Cognitive System for User-Adaptive Service Robots, In proceedings of 20 th IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man 2011) at Atlanta, GA, USA, July 2011, pp. 59-64.
This paper proposes a dual-layer user model to generate descriptive service recommendations for user-adaptive service robots. The user model represents user preferences as the associative memory in the bottom-layer and association rules in the top-layer. The learning and inference processes in the two layers, and the bottom-up rule extraction process, are explained. The proposed user model was applied to a user-adaptive coffee menu recommendation system, and the quantitative and qualitative performances of the user-adaptive and descriptive recommendation system were evaluated by comparison with non-descriptive and random recommendation methods.